Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations4585125
Missing cells93475
Missing cells (%)0.1%
Duplicate rows771763
Duplicate rows (%)16.8%
Total size in memory1.5 GiB
Average record size in memory348.6 B

Variable types

Categorical21
Numeric3
Unsupported2

Alerts

is_Origination_channel_tpo has constant value "0.0"Constant
Dataset has 771763 (16.8%) duplicate rowsDuplicates
is_First_time_homeowner is highly overall correlated with is_First_time_homeowner_NoHigh correlation
is_First_time_homeowner_No is highly overall correlated with is_First_time_homeownerHigh correlation
is_Loan_purpose_cash is highly overall correlated with is_Loan_purpose_purcHigh correlation
is_Loan_purpose_noca is highly overall correlated with is_Loan_purpose_purcHigh correlation
is_Loan_purpose_purc is highly overall correlated with is_Loan_purpose_cash and 1 other fieldsHigh correlation
is_Occupancy_status_inve is highly overall correlated with is_Occupancy_status_primHigh correlation
is_Occupancy_status_prim is highly overall correlated with is_Occupancy_status_inve and 1 other fieldsHigh correlation
is_Occupancy_status_seco is highly overall correlated with is_Occupancy_status_primHigh correlation
is_Origination_channel_corr is highly overall correlated with is_Origination_channel_retaHigh correlation
is_Origination_channel_reta is highly overall correlated with is_Origination_channel_corrHigh correlation
is_Property_type_pud is highly overall correlated with is_Property_type_singHigh correlation
is_Property_type_sing is highly overall correlated with is_Property_type_pudHigh correlation
Number_of_units is highly imbalanced (91.3%)Imbalance
is_Occupancy_status_inve is highly imbalanced (62.2%)Imbalance
is_Occupancy_status_seco is highly imbalanced (76.6%)Imbalance
is_Property_type_cond is highly imbalanced (58.2%)Imbalance
is_Property_type_coop is highly imbalanced (98.6%)Imbalance
is_Property_type_manu is highly imbalanced (96.3%)Imbalance
DFlag is highly imbalanced (89.0%)Imbalance
Debt_to_income has 93317 (2.0%) missing valuesMissing
Credit_Score is highly skewed (γ1 = 56.1532274)Skewed
CLoan_to_value is an unsupported type, check if it needs cleaning or further analysisUnsupported
OLoan_to_value is an unsupported type, check if it needs cleaning or further analysisUnsupported
Mortgage_Insurance has 3200202 (69.8%) zerosZeros

Reproduction

Analysis started2025-10-24 01:12:46.699309
Analysis finished2025-10-24 01:15:43.405826
Duration2 minutes and 56.71 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Origination_date
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size293.0 MiB
2019-10-01
533390 
2019-07-01
524439 
2016-07-01
463598 
2016-10-01
449052 
2016-04-01
415979 
Other values (7)
2198667 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters45851250
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-01-01
2nd row2016-01-01
3rd row2016-01-01
4th row2016-01-01
5th row2016-01-01

Common Values

ValueCountFrequency (%)
2019-10-01533390
11.6%
2019-07-01524439
11.4%
2016-07-01463598
10.1%
2016-10-01449052
9.8%
2016-04-01415979
9.1%
2019-04-01404381
8.8%
2018-04-01345799
7.5%
2018-07-01318510
6.9%
2016-01-01297139
6.5%
2018-01-01283276
6.2%
Other values (2)549562
12.0%

Length

2025-10-23T21:15:43.483900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-10-01533390
11.6%
2019-07-01524439
11.4%
2016-07-01463598
10.1%
2016-10-01449052
9.8%
2016-04-01415979
9.1%
2019-04-01404381
8.8%
2018-04-01345799
7.5%
2018-07-01318510
6.9%
2016-01-01297139
6.5%
2018-01-01283276
6.2%
Other values (2)549562
12.0%

Most occurring characters

ValueCountFrequency (%)
013755375
30.0%
111282669
24.6%
-9170250
20.0%
24585125
 
10.0%
91735667
 
3.8%
61625768
 
3.5%
71306547
 
2.8%
81223690
 
2.7%
41166159
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)45851250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
013755375
30.0%
111282669
24.6%
-9170250
20.0%
24585125
 
10.0%
91735667
 
3.8%
61625768
 
3.5%
71306547
 
2.8%
81223690
 
2.7%
41166159
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)45851250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
013755375
30.0%
111282669
24.6%
-9170250
20.0%
24585125
 
10.0%
91735667
 
3.8%
61625768
 
3.5%
71306547
 
2.8%
81223690
 
2.7%
41166159
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)45851250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
013755375
30.0%
111282669
24.6%
-9170250
20.0%
24585125
 
10.0%
91735667
 
3.8%
61625768
 
3.5%
71306547
 
2.8%
81223690
 
2.7%
41166159
 
2.5%

Credit_Score
Real number (ℝ)

Skewed 

Distinct389
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean750.80431
Minimum309
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.0 MiB
2025-10-23T21:15:43.544423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum309
5-th percentile664
Q1717
median757
Q3786
95-th percentile808
Maximum10000
Range9691
Interquartile range (IQR)69

Descriptive statistics

Standard deviation149.31935
Coefficient of variation (CV)0.19887919
Kurtosis3476.467
Mean750.80431
Median Absolute Deviation (MAD)33
Skewness56.153227
Sum3.4425316 × 109
Variance22296.27
MonotonicityNot monotonic
2025-10-23T21:15:43.604682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80152972
 
1.2%
79050550
 
1.1%
79750388
 
1.1%
78749503
 
1.1%
79147685
 
1.0%
80246234
 
1.0%
79645184
 
1.0%
79844838
 
1.0%
78444052
 
1.0%
80043602
 
1.0%
Other values (379)4110117
89.6%
ValueCountFrequency (%)
3091
< 0.1%
3861
< 0.1%
4271
< 0.1%
4361
< 0.1%
4401
< 0.1%
4411
< 0.1%
4451
< 0.1%
4491
< 0.1%
4541
< 0.1%
4562
< 0.1%
ValueCountFrequency (%)
10000387
< 0.1%
9999697
< 0.1%
8501
 
< 0.1%
8431
 
< 0.1%
8401
 
< 0.1%
8399
 
< 0.1%
8381
 
< 0.1%
8374
 
< 0.1%
8353
 
< 0.1%
83413
 
< 0.1%

Mortgage_Insurance
Real number (ℝ)

Zeros 

Distinct36
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.4783022
Minimum0
Maximum53
Zeros3200202
Zeros (%)69.8%
Negative0
Negative (%)0.0%
Memory size35.0 MiB
2025-10-23T21:15:43.661504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q318
95-th percentile30
Maximum53
Range53
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.912594
Coefficient of variation (CV)1.5929543
Kurtosis-0.68898435
Mean7.4783022
Median Absolute Deviation (MAD)0
Skewness1.0768405
Sum34288928
Variance141.90989
MonotonicityNot monotonic
2025-10-23T21:15:43.719740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
03200202
69.8%
25561799
 
12.3%
30559005
 
12.2%
12195937
 
4.3%
1822102
 
0.5%
622047
 
0.5%
1612180
 
0.3%
3510661
 
0.2%
20992
 
< 0.1%
1792
 
< 0.1%
Other values (26)105
 
< 0.1%
ValueCountFrequency (%)
03200202
69.8%
11
 
< 0.1%
622047
 
0.5%
103
 
< 0.1%
12195937
 
4.3%
131
 
< 0.1%
158
 
< 0.1%
1612180
 
0.3%
1792
 
< 0.1%
1822102
 
0.5%
ValueCountFrequency (%)
531
 
< 0.1%
521
 
< 0.1%
501
 
< 0.1%
441
 
< 0.1%
433
 
< 0.1%
4014
 
< 0.1%
391
 
< 0.1%
383
 
< 0.1%
373
 
< 0.1%
3510661
0.2%

Number_of_units
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
1.0
4486761 
2.0
 
69640
3.0
 
16204
4.0
 
12520

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.04486761
97.9%
2.069640
 
1.5%
3.016204
 
0.4%
4.012520
 
0.3%

Length

2025-10-23T21:15:43.768526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:43.816976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.04486761
97.9%
2.069640
 
1.5%
3.016204
 
0.4%
4.012520
 
0.3%

Most occurring characters

ValueCountFrequency (%)
.4585125
33.3%
04585125
33.3%
14486761
32.6%
269640
 
0.5%
316204
 
0.1%
412520
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.4585125
33.3%
04585125
33.3%
14486761
32.6%
269640
 
0.5%
316204
 
0.1%
412520
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.4585125
33.3%
04585125
33.3%
14486761
32.6%
269640
 
0.5%
316204
 
0.1%
412520
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.4585125
33.3%
04585125
33.3%
14486761
32.6%
269640
 
0.5%
316204
 
0.1%
412520
 
0.1%

CLoan_to_value
Unsupported

Rejected  Unsupported 

Missing108
Missing (%)< 0.1%
Memory size213.3 MiB

Debt_to_income
Real number (ℝ)

Missing 

Distinct54
Distinct (%)< 0.1%
Missing93317
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean35.020002
Minimum1
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.0 MiB
2025-10-23T21:15:43.866114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q128
median36
Q343
95-th percentile49
Maximum61
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.4154345
Coefficient of variation (CV)0.26885877
Kurtosis-0.51000317
Mean35.020002
Median Absolute Deviation (MAD)7
Skewness-0.47273383
Sum1.5730312 × 108
Variance88.650407
MonotonicityNot monotonic
2025-10-23T21:15:43.925603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44210620
 
4.6%
45209992
 
4.6%
43196250
 
4.3%
42185748
 
4.1%
41179006
 
3.9%
40174045
 
3.8%
39169391
 
3.7%
38165089
 
3.6%
37160898
 
3.5%
36156510
 
3.4%
Other values (44)2684259
58.5%
ValueCountFrequency (%)
1341
 
< 0.1%
2417
 
< 0.1%
3577
 
< 0.1%
4787
 
< 0.1%
51205
 
< 0.1%
61784
 
< 0.1%
72626
 
0.1%
83816
0.1%
95542
0.1%
107665
0.2%
ValueCountFrequency (%)
611
 
< 0.1%
542
 
< 0.1%
531
 
< 0.1%
515
 
< 0.1%
50110661
2.4%
49117309
2.6%
48108734
2.4%
47107368
2.3%
46109147
2.4%
45209992
4.6%

OLoan_to_value
Unsupported

Rejected  Unsupported 

Missing45
Missing (%)< 0.1%
Memory size213.3 MiB

Single_borrower
Categorical

Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size262.4 MiB
1.0
2414563 
0.0
2170560 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755369
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.02414563
52.7%
0.02170560
47.3%
(Missing)2
 
< 0.1%

Length

2025-10-23T21:15:43.981729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.010969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.02414563
52.7%
0.02170560
47.3%

Most occurring characters

ValueCountFrequency (%)
06755683
49.1%
.4585123
33.3%
12414563
 
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06755683
49.1%
.4585123
33.3%
12414563
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06755683
49.1%
.4585123
33.3%
12414563
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06755683
49.1%
.4585123
33.3%
12414563
 
17.6%

is_Loan_purpose_purc
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
1.0
2476620 
0.0
2108505 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.02476620
54.0%
0.02108505
46.0%

Length

2025-10-23T21:15:44.053658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.083634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.02476620
54.0%
0.02108505
46.0%

Most occurring characters

ValueCountFrequency (%)
06693630
48.7%
.4585125
33.3%
12476620
 
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06693630
48.7%
.4585125
33.3%
12476620
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06693630
48.7%
.4585125
33.3%
12476620
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06693630
48.7%
.4585125
33.3%
12476620
 
18.0%

is_Loan_purpose_cash
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
3607740 
1.0
977385 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03607740
78.7%
1.0977385
 
21.3%

Length

2025-10-23T21:15:44.440634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.465379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03607740
78.7%
1.0977385
 
21.3%

Most occurring characters

ValueCountFrequency (%)
08192865
59.6%
.4585125
33.3%
1977385
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08192865
59.6%
.4585125
33.3%
1977385
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08192865
59.6%
.4585125
33.3%
1977385
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08192865
59.6%
.4585125
33.3%
1977385
 
7.1%

is_Loan_purpose_noca
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
3454005 
1.0
1131120 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.03454005
75.3%
1.01131120
 
24.7%

Length

2025-10-23T21:15:44.501441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.531509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03454005
75.3%
1.01131120
 
24.7%

Most occurring characters

ValueCountFrequency (%)
08039130
58.4%
.4585125
33.3%
11131120
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08039130
58.4%
.4585125
33.3%
11131120
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08039130
58.4%
.4585125
33.3%
11131120
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08039130
58.4%
.4585125
33.3%
11131120
 
8.2%

is_First_time_homeowner
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
3621607 
1.0
963518 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03621607
79.0%
1.0963518
 
21.0%

Length

2025-10-23T21:15:44.568546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.598875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03621607
79.0%
1.0963518
 
21.0%

Most occurring characters

ValueCountFrequency (%)
08206732
59.7%
.4585125
33.3%
1963518
 
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08206732
59.7%
.4585125
33.3%
1963518
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08206732
59.7%
.4585125
33.3%
1963518
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08206732
59.7%
.4585125
33.3%
1963518
 
7.0%

is_First_time_homeowner_No
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
1.0
3621606 
0.0
963519 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.03621606
79.0%
0.0963519
 
21.0%

Length

2025-10-23T21:15:44.635155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.662457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.03621606
79.0%
0.0963519
 
21.0%

Most occurring characters

ValueCountFrequency (%)
05548644
40.3%
.4585125
33.3%
13621606
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05548644
40.3%
.4585125
33.3%
13621606
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05548644
40.3%
.4585125
33.3%
13621606
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05548644
40.3%
.4585125
33.3%
13621606
26.3%

is_Occupancy_status_prim
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
1.0
4074346 
0.0
510779 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.04074346
88.9%
0.0510779
 
11.1%

Length

2025-10-23T21:15:44.701543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.727169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.04074346
88.9%
0.0510779
 
11.1%

Most occurring characters

ValueCountFrequency (%)
05095904
37.0%
.4585125
33.3%
14074346
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
05095904
37.0%
.4585125
33.3%
14074346
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
05095904
37.0%
.4585125
33.3%
14074346
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
05095904
37.0%
.4585125
33.3%
14074346
29.6%

is_Occupancy_status_inve
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
4249309 
1.0
 
335816

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04249309
92.7%
1.0335816
 
7.3%

Length

2025-10-23T21:15:44.766924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.792661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04249309
92.7%
1.0335816
 
7.3%

Most occurring characters

ValueCountFrequency (%)
08834434
64.2%
.4585125
33.3%
1335816
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08834434
64.2%
.4585125
33.3%
1335816
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08834434
64.2%
.4585125
33.3%
1335816
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08834434
64.2%
.4585125
33.3%
1335816
 
2.4%

is_Occupancy_status_seco
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
4410162 
1.0
 
174963

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04410162
96.2%
1.0174963
 
3.8%

Length

2025-10-23T21:15:44.828467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.857169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04410162
96.2%
1.0174963
 
3.8%

Most occurring characters

ValueCountFrequency (%)
08995287
65.4%
.4585125
33.3%
1174963
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08995287
65.4%
.4585125
33.3%
1174963
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08995287
65.4%
.4585125
33.3%
1174963
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08995287
65.4%
.4585125
33.3%
1174963
 
1.3%

is_Origination_channel_reta
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
1.0
2604905 
0.0
1980220 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.02604905
56.8%
0.01980220
43.2%

Length

2025-10-23T21:15:44.889555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.919698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.02604905
56.8%
0.01980220
43.2%

Most occurring characters

ValueCountFrequency (%)
06565345
47.7%
.4585125
33.3%
12604905
 
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06565345
47.7%
.4585125
33.3%
12604905
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06565345
47.7%
.4585125
33.3%
12604905
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06565345
47.7%
.4585125
33.3%
12604905
 
18.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
4076285 
1.0
508840 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.04076285
88.9%
1.0508840
 
11.1%

Length

2025-10-23T21:15:44.956025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:44.985406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04076285
88.9%
1.0508840
 
11.1%

Most occurring characters

ValueCountFrequency (%)
08661410
63.0%
.4585125
33.3%
1508840
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08661410
63.0%
.4585125
33.3%
1508840
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08661410
63.0%
.4585125
33.3%
1508840
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08661410
63.0%
.4585125
33.3%
1508840
 
3.7%

is_Origination_channel_corr
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
3113750 
1.0
1471375 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03113750
67.9%
1.01471375
32.1%

Length

2025-10-23T21:15:45.022267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:45.046987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03113750
67.9%
1.01471375
32.1%

Most occurring characters

ValueCountFrequency (%)
07698875
56.0%
.4585125
33.3%
11471375
 
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07698875
56.0%
.4585125
33.3%
11471375
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07698875
56.0%
.4585125
33.3%
11471375
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07698875
56.0%
.4585125
33.3%
11471375
 
10.7%

is_Origination_channel_tpo
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
4585125 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04585125
100.0%

Length

2025-10-23T21:15:45.083452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:45.107116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04585125
100.0%

Most occurring characters

ValueCountFrequency (%)
09170250
66.7%
.4585125
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09170250
66.7%
.4585125
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09170250
66.7%
.4585125
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09170250
66.7%
.4585125
33.3%

is_Property_type_cond
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
4198119 
1.0
 
387006

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04198119
91.6%
1.0387006
 
8.4%

Length

2025-10-23T21:15:45.145726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:45.175308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04198119
91.6%
1.0387006
 
8.4%

Most occurring characters

ValueCountFrequency (%)
08783244
63.9%
.4585125
33.3%
1387006
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
08783244
63.9%
.4585125
33.3%
1387006
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
08783244
63.9%
.4585125
33.3%
1387006
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
08783244
63.9%
.4585125
33.3%
1387006
 
2.8%

is_Property_type_coop
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
4579133 
1.0
 
5992

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04579133
99.9%
1.05992
 
0.1%

Length

2025-10-23T21:15:45.210239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:45.234884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04579133
99.9%
1.05992
 
0.1%

Most occurring characters

ValueCountFrequency (%)
09164258
66.6%
.4585125
33.3%
15992
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09164258
66.6%
.4585125
33.3%
15992
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09164258
66.6%
.4585125
33.3%
15992
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09164258
66.6%
.4585125
33.3%
15992
 
< 0.1%

is_Property_type_manu
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
4567418 
1.0
 
17707

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04567418
99.6%
1.017707
 
0.4%

Length

2025-10-23T21:15:45.264615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:45.301881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04567418
99.6%
1.017707
 
0.4%

Most occurring characters

ValueCountFrequency (%)
09152543
66.5%
.4585125
33.3%
117707
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09152543
66.5%
.4585125
33.3%
117707
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09152543
66.5%
.4585125
33.3%
117707
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09152543
66.5%
.4585125
33.3%
117707
 
0.1%

is_Property_type_pud
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
3315912 
1.0
1269213 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.03315912
72.3%
1.01269213
 
27.7%

Length

2025-10-23T21:15:45.337991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:45.365688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.03315912
72.3%
1.01269213
 
27.7%

Most occurring characters

ValueCountFrequency (%)
07901037
57.4%
.4585125
33.3%
11269213
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07901037
57.4%
.4585125
33.3%
11269213
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07901037
57.4%
.4585125
33.3%
11269213
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07901037
57.4%
.4585125
33.3%
11269213
 
9.2%

is_Property_type_sing
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
1.0
2905207 
0.0
1679918 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.02905207
63.4%
0.01679918
36.6%

Length

2025-10-23T21:15:45.400208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:45.428344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.02905207
63.4%
0.01679918
36.6%

Most occurring characters

ValueCountFrequency (%)
06265043
45.5%
.4585125
33.3%
12905207
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06265043
45.5%
.4585125
33.3%
12905207
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06265043
45.5%
.4585125
33.3%
12905207
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06265043
45.5%
.4585125
33.3%
12905207
21.1%

DFlag
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.4 MiB
0.0
4518144 
1.0
 
66981

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13755375
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04518144
98.5%
1.066981
 
1.5%

Length

2025-10-23T21:15:45.464682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-23T21:15:45.489415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.04518144
98.5%
1.066981
 
1.5%

Most occurring characters

ValueCountFrequency (%)
09103269
66.2%
.4585125
33.3%
166981
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
09103269
66.2%
.4585125
33.3%
166981
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
09103269
66.2%
.4585125
33.3%
166981
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13755375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
09103269
66.2%
.4585125
33.3%
166981
 
0.5%

Interactions

2025-10-23T21:15:25.775236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T21:15:22.150672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T21:15:24.042477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T21:15:26.385028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T21:15:22.883699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T21:15:24.591796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T21:15:26.971135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T21:15:23.455286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-23T21:15:25.163335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-23T21:15:45.525194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Credit_ScoreDFlagDebt_to_incomeMortgage_InsuranceNumber_of_unitsOrigination_dateSingle_borroweris_First_time_homeowneris_First_time_homeowner_Nois_Loan_purpose_cashis_Loan_purpose_nocais_Loan_purpose_purcis_Occupancy_status_inveis_Occupancy_status_primis_Occupancy_status_secois_Origination_channel_brokis_Origination_channel_corris_Origination_channel_retais_Property_type_condis_Property_type_coopis_Property_type_manuis_Property_type_pudis_Property_type_sing
Credit_Score1.0000.000-0.154-0.0660.0020.0120.0070.0150.0150.0070.0050.0100.0040.0050.0030.0040.0060.0080.0020.0020.0000.0000.001
DFlag0.0001.0000.0520.0330.0110.1110.0270.0170.0170.0050.0040.0010.0040.0080.0080.0020.0250.0240.0010.0000.0000.0010.002
Debt_to_income-0.1540.0521.0000.0590.0240.0290.0910.0260.0260.0330.0830.0440.0570.0570.0270.0360.0170.0370.0270.0080.0060.0150.027
Mortgage_Insurance-0.0660.0330.0591.0000.0410.0440.0740.4140.4140.3260.2050.4250.1720.1880.0830.0280.0840.0750.0450.0130.0090.0330.027
Number_of_units0.0020.0110.0240.0411.0000.0100.0210.0320.0320.0290.0060.0190.2590.1970.0290.0240.0040.0130.0450.0050.0090.0890.110
Origination_date0.0120.1110.0290.0440.0101.0000.0350.1330.1330.0560.2290.2260.0440.0370.0220.0430.0550.0760.0110.0070.0100.0180.022
Single_borrower0.0070.0270.0910.0740.0210.0351.0000.0730.0730.0100.0140.0210.0160.0170.0490.0140.0010.0080.0880.0130.0020.0260.027
is_First_time_homeowner0.0150.0170.0260.4140.0320.1330.0731.0001.0000.2680.2950.4760.1450.1830.1030.0180.0630.0720.0850.0360.0010.0160.037
is_First_time_homeowner_No0.0150.0170.0260.4140.0320.1330.0731.0001.0000.2680.2950.4760.1450.1830.1030.0180.0630.0720.0850.0360.0010.0160.037
is_Loan_purpose_cash0.0070.0050.0330.3260.0290.0560.0100.2680.2681.0000.2980.5640.0400.0010.0570.0190.0730.0810.0720.0090.0110.0800.117
is_Loan_purpose_noca0.0050.0040.0830.2050.0060.2290.0140.2950.2950.2981.0000.6200.0220.0460.0450.0200.0620.0460.0280.0120.0060.0270.042
is_Loan_purpose_purc0.0100.0010.0440.4250.0190.2260.0210.4760.4760.5640.6201.0000.0140.0410.0860.0010.1140.1060.0830.0180.0040.0890.133
is_Occupancy_status_inve0.0040.0040.0570.1720.2590.0440.0160.1450.1450.0400.0220.0141.0000.7940.0560.0240.0200.0040.0340.0100.0170.0460.026
is_Occupancy_status_prim0.0050.0080.0570.1880.1970.0370.0170.1830.1830.0010.0460.0410.7941.0000.5630.0130.0180.0090.0710.0100.0060.0310.011
is_Occupancy_status_seco0.0030.0080.0270.0830.0290.0220.0490.1030.1030.0570.0450.0860.0560.5631.0000.0110.0020.0090.0700.0030.0130.0120.053
is_Origination_channel_brok0.0040.0020.0360.0280.0240.0430.0140.0180.0180.0190.0200.0010.0240.0130.0111.0000.2430.4050.0260.0030.0140.0030.016
is_Origination_channel_corr0.0060.0250.0170.0840.0040.0550.0010.0630.0630.0730.0620.1140.0200.0180.0020.2431.0000.7880.0020.0120.0270.0460.037
is_Origination_channel_reta0.0080.0240.0370.0750.0130.0760.0080.0720.0720.0810.0460.1060.0040.0090.0090.4050.7881.0000.0140.0090.0350.0450.045
is_Property_type_cond0.0020.0010.0270.0450.0450.0110.0880.0850.0850.0720.0280.0830.0340.0710.0700.0260.0020.0141.0000.0110.0190.1880.399
is_Property_type_coop0.0020.0000.0080.0130.0050.0070.0130.0360.0360.0090.0120.0180.0100.0100.0030.0030.0120.0090.0111.0000.0020.0220.048
is_Property_type_manu0.0000.0000.0060.0090.0090.0100.0020.0010.0010.0110.0060.0040.0170.0060.0130.0140.0270.0350.0190.0021.0000.0390.082
is_Property_type_pud0.0000.0010.0150.0330.0890.0180.0260.0160.0160.0800.0270.0890.0460.0310.0120.0030.0460.0450.1880.0220.0391.0000.814
is_Property_type_sing0.0010.0020.0270.0270.1100.0220.0270.0370.0370.1170.0420.1330.0260.0110.0530.0160.0370.0450.3990.0480.0820.8141.000

Missing values

2025-10-23T21:15:27.520421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-23T21:15:31.617655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-23T21:15:39.986328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Origination_dateCredit_ScoreMortgage_InsuranceNumber_of_unitsCLoan_to_valueDebt_to_incomeOLoan_to_valueSingle_borroweris_Loan_purpose_purcis_Loan_purpose_cashis_Loan_purpose_nocais_First_time_homeowneris_First_time_homeowner_Nois_Occupancy_status_primis_Occupancy_status_inveis_Occupancy_status_secois_Origination_channel_retais_Origination_channel_brokis_Origination_channel_corris_Origination_channel_tpois_Property_type_condis_Property_type_coopis_Property_type_manuis_Property_type_pudis_Property_type_singDFlag
02016-01-01787.025.01.08836.0880.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
12016-01-01779.00.01.07536.0750.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
22016-01-01816.030.01.09534.0950.01.00.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
32016-01-01740.025.01.09044.0901.01.00.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
42016-01-01786.030.01.09228.0921.00.00.01.00.01.01.00.00.00.01.00.00.00.00.00.01.00.00.0
52016-01-01806.00.01.06047.0601.01.00.00.00.01.01.00.00.00.00.01.00.00.00.00.01.00.00.0
62016-01-01757.00.01.01746.0171.01.00.00.00.01.00.00.01.00.01.00.00.00.00.00.01.00.00.0
72016-01-01753.00.01.06250.0620.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
82016-01-01778.00.01.08018.0801.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
92016-01-01745.00.01.05546.0551.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.01.00.00.0
Origination_dateCredit_ScoreMortgage_InsuranceNumber_of_unitsCLoan_to_valueDebt_to_incomeOLoan_to_valueSingle_borroweris_Loan_purpose_purcis_Loan_purpose_cashis_Loan_purpose_nocais_First_time_homeowneris_First_time_homeowner_Nois_Occupancy_status_primis_Occupancy_status_inveis_Occupancy_status_secois_Origination_channel_retais_Origination_channel_brokis_Origination_channel_corris_Origination_channel_tpois_Property_type_condis_Property_type_coopis_Property_type_manuis_Property_type_pudis_Property_type_singDFlag
45851152019-10-01715.00.01.080.048.080.01.01.00.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
45851162019-10-01665.00.01.080.031.080.01.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.00.01.00.0
45851172019-10-01672.00.01.077.031.077.00.00.01.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
45851182019-10-01723.00.01.080.042.080.01.00.01.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
45851192019-10-01695.00.01.068.043.068.01.00.01.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
45851202019-10-01713.00.01.037.028.037.00.00.01.00.00.01.01.00.00.00.00.01.00.00.00.00.00.01.00.0
45851212019-10-01690.00.01.040.040.040.00.00.01.00.00.01.01.00.00.00.00.01.00.00.00.00.01.00.00.0
45851222019-10-01653.00.01.069.045.069.00.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
45851232019-10-01661.00.01.043.032.043.00.00.01.00.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0
45851242019-10-01720.00.01.063.042.063.01.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.0

Duplicate rows

Most frequently occurring

Origination_dateCredit_ScoreMortgage_InsuranceNumber_of_unitsDebt_to_incomeSingle_borroweris_Loan_purpose_purcis_Loan_purpose_cashis_Loan_purpose_nocais_First_time_homeowneris_First_time_homeowner_Nois_Occupancy_status_primis_Occupancy_status_inveis_Occupancy_status_secois_Origination_channel_retais_Origination_channel_brokis_Origination_channel_corris_Origination_channel_tpois_Property_type_condis_Property_type_coopis_Property_type_manuis_Property_type_pudis_Property_type_singDFlag# duplicates
1958882016-07-01809.00.01.0NaN1.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.090
442312016-01-01809.00.01.0NaN1.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.069
1150012016-04-01809.00.01.0NaN1.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.068
451632016-01-01813.00.01.0NaN1.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.061
1097552016-04-01801.00.01.0NaN1.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.061
1097472016-04-01801.00.01.0NaN0.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.059
2739432016-10-01813.00.01.0NaN1.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.055
2735362016-10-01812.00.01.0NaN1.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.054
399252016-01-01799.00.01.0NaN1.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.052
416322016-01-01802.00.01.0NaN1.00.00.01.00.01.01.00.00.01.00.00.00.00.00.00.00.01.00.052